Adaptive convolutional neural network using N-gram for spatial object recognition

• Published in: Earth science informatics Vol. 12; no. 4; pp. 525 - 540
• Main Authors: Bapu, J. Joshua, Florinabel, D. Jemi, Robinson, Y. Harold, Julie, E. Golden, Kumar, Raghvendra, Ngoc, Vo Truong Nhu, Son, Le Hoang, Tuan, Tran Manh, Giap, Cu Nguyen
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2019; Springer Nature B.V
• Subjects: Artificial neural networks; Cognition; Earth and Environmental Science; Earth Sciences; Earth System Sciences; Experimentation; Image detection; Image processing; Image resolution; Information Systems Applications (incl.Internet); Learning; Mathematical analysis; Mathematical models; Neural networks; Object recognition; Ontology; Perception; Remote sensing; Research Article; Satellite imagery; Satellites; Simulation and Modeling; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Spatial data; Adaptive convolutional neural network; N-gram; Spatial object recognition; Geographical objects

Remote sensing applications are playing a vital role to improve the commercial satellite imagery with high resolution. In the spatial information system, object detection is the basic needs for computing the mathematical model. Geographical object related analysis for the image is used to gather dat...